Highways consist of many large structures requiring vigilant inspection and maintenance. While significant research attention has been focused on the health management of bridges, comparatively less attention has been paid to other highway structures including retaining walls. In the United States, there has been a recent emphasis on extending the use of risk management methods to the extremely large national inventory of retaining wall structures. In this paper, a long-term wireless monitoring system is developed as a cost-efficient approach to collecting data and information required for risk assessment of retaining wall structures. The study focuses on two reinforced concrete (RC) retaining walls to highlight the monitoring system design and to illustrate how measurement data offers insight to wall performance. The first wall is a caisson supported retaining wall along the M-10 freeway in Detroit, MI; the second is a classical reinforced concrete cantilever wall along I-696 in Southfield, MI. The wireless monitoring system installed on each wall system uses a cellular-based wireless sensor node termed Urbano that is solar powered and customized to measure wall tilt using inclinometers, wall strain using strain gages, and wall temperature using thermistors. The monitoring systems have been valuable in assessing the behavior of the M-10 and I-696 wall systems for a broader risk management framework. The monitoring results reveal both wall systems are operating as designed with limited tilt and strain responses to normal environmental factors including moisture and temperature.

The widespread availability of cost-effective sensing technologies is translating into an increasing number of highway bridges being instrumented with structural health monitoring (SHM) systems. Current bridge SHM systems are only capable of measuring bridge responses and lack the ability to directly measure the traffic loads inducing bridge responses. The output-only nature of the monitoring data available often leaves damage detection algorithms ill-posed and incapable of robust detection. Attempting to overcome this challenge, this study leverages state-of-the-art computer vision techniques to establish a means of reliably acquiring load data associated with the trucks inducing bridge responses. Using a cyberenabled highway corridor consisting of cameras, bridge monitoring systems, and weigh-in-motion (WIM) stations, computer vision methods are used to track trucks as they excite bridges and pass WIM stations where their weight parameters are acquired. Convolutional neural network (CNN) methods are used to develop automated vehicle detectors embedded in GPU-enabled cameras along highway corridors to identify and track trucks from real-time traffic video. Detected vehicles are used to trigger the bridge monitoring systems to ensure structural responses are captured when trucks pass. In the study, multiple one-stage object detection CNN architectures have been trained using a customized dataset to identify various types of vehicles captured at multiple locations along a highway corridor. YOLOv3 is selected for its competitive speed and precision in identifying trucks. A customized CNN-based embedding network is trained following a triplet architecture to convert each truck image into a feature vector and the Euclidean distance of two feature vectors is used as a measure of truck similarity for reidentification purposes. The performance of the CNN-based feature extract is proved to be more robust than a hand-crafted method. Reidentification of the same vehicle allows truck weights measured at the WIM station to be associated with measured bridge responses collected by bridge monitoring systems.

Due to their mobility and autonomy, unmanned aerial vehicles (UAVs) provide an unprecedented opportunity to perform data gathering in a wide array of civil engineering applications such as visual inspection of infrastructure. Given their versatility, the role of UAVs can be expanded by leveraging their autonomous operations to deploy wireless sensing resources. This can be especially valuable in numerous field applications such as shear wave velocity (Vs) assessment of the subsurface. This study explores the feasibility of automating the autonomous placement and pickup of wireless geophone sensors using UAVs for multichannel analysis of surface waves (MASW) for subsurface characterization. Typically, autonomous navigation of UAVs is based on the fusion of inertial sensors and GPS to control the UAV flight trajectory. However, this approach is not sufficiently accurate for missions requiring precision placement and pickup of payloads (such as sensors). Hence, computer vision using fiducial markers can be used to augment traditional inertial sensing to add accuracy to the localization of the UAV relative to payloads. In this study, we use a set of fiducial markers of varying sizes as tracking targets during navigation missions. Pose information extracted from the marker images are integrated into a sensor fusion controller based on the Kalman filter. The work conducts field validation of the proposed computer vision navigation method showing accuracy of the UAV landing on a user defined target within 10 cm; as the UAV descends, smaller fiducial markers are shown to increase the precision of the UAV placement on the ground.

In recent years, interest has grown in the development of sensing skins for structural health monitoring (SHM) with electrical impedance tomography (EIT) used to image skin properties. The computational e
ort associated with the inverse solver of EIT is very large and sometimes the final reconstructed strain map derived does not correspond to the true state of the system. To reduce the large computational effort associated with EIT reconstruction of large unpatterned thin films, this study fabricates a patterned spatial strain sensor made from single-walled carbon nanotube (SWCNT) nanocomposite films. The thin nanocomposite strain sensing films are fabricated on a flexible polyimide substrate by using a layer-by-layer (LbL) deposition process. Rather than using a plain/unpatterned film as previously proposed in EIT-based approaches, a grid of piezoresistive nanocomposite strip elements are patterned. The 2D grid arrangement of the nanocomposite sensing elements is achieved using optical lithography. Metal electrodes are deployed at the boundary nodes by physical vapor deposition (PVD) and are connected to wires for controlled current injection and electric potential measurements. In order to infer the strain distribution of a structure using the patterned strain sensing skin, there is a need to have a robust inverse solver. Ohm's and Kircho's laws are used to derived the Neumann-to-Dirichlet map of the rectangular resistor network. The Neumann-to-Dirichlet operator is represented by a matrix which is a function of the resistance distribution. The inverse solution obtains the resistance of each grid element by updating the Neumann-to-Dirichlet operator to drive convergence between the boundary potential measurement taken of the film and those predicted by the forward solution. The inverse solver has been validated by simulations and experiments of a square resistor network of 3 by 3 (node by node) and 4 by 4 resistor grids. Preliminary results show that the proposed inverse solver is accurate for 2D strain measurement using the patterned strain sensing grid.

Osseointegration of a prosthesis offers a novel approach to enhancing the quality of life of an amputee because it makes an artificial limb an integral part of their body. While osseointegrated prostheses offer amputees many benefits, long-term health of the prosthesis fixture in the host bone is a concern. In particular, overloading of the fixture can result in damage to the host bone including bone fracture. This study offers a novel sensing strategy implemented on the percutaneous end of an osseointegrated prosthesis. Piezoelectric actuators are used to generate elastic stress waves in the prosthesis to interrogate the integrity of the prosthesis-bone interface. In this study, flexural mode Rayleigh waves are introduced in the prosthesis to identify the existence and location of fracture in the host bone. A prosthetic model consisting of a titanium rod implanted in a synthetic sawbone with piezoelectric wafer elements bonded to the rod surface is used to validate the proposed approach. The work reveals the waveforms associated with flexural wave modes are directly correlated to bone fracture occurring at the prosthesis-bone interface with fracture location identifiable in the reflect wave features.

A novel d36-type piezoelectric wafer fabricated from lead magnesium niobate-lead titanate (PMN-PT) is explored for the generation of in-plane horizontal shear waves in plate structures. The study focuses on the development of a linear phased array (PA) of PMN-PT wafers to improve the damage detection capabilities of a structural health monitoring (SHM) system. An attractive property of in-plane horizontal shear waves is that they are nondispersive yet sensitive to damage. This study characterizes the directionality of body waves (Lamb and horizontal shear) created by a single PMN-PT wafer bonded to the surface of a metallic plate structure. Second, a linear PA is designed from PMN-PT wafers to steer and focus Lamb and horizontal shear waves in a plate structure. Numerical studies are conducted to explore the capabilities of a PMN-PT-based PA to detect damage in aluminum plates. Numerical simulations are conducted using the Local Interaction Simulation Approach (LISA) implemented on a parallelized graphical processing unit (GPU) for high-speed execution. Numerical studies are further validated using experimental tests conducted with a linear PA. The study confirms the ability of an PMN-PT phased array to accurately detect and localize damage in aluminum plates.

The ability to quantitatively assess the condition of railroad bridges facilitates objective evaluation of their robustness in the face of hazard events. Of particular importance is the need to assess the condition of railroad bridges in networks that are exposed to multiple hazards. Data collected from structural health monitoring (SHM) can be used to better maintain a structure by prompting preventative (rather than reactive) maintenance strategies and supplying quantitative information to aid in recovery. To that end, a wireless monitoring system is validated and installed on the Harahan Bridge which is a hundred-year-old long-span railroad truss bridge that crosses the Mississippi River near Memphis, TN. This bridge is exposed to multiple hazards including scour, vehicle/barge impact, seismic activity, and aging. The instrumented sensing system targets non-redundant structural components and areas of the truss and floor system that bridge managers are most concerned about based on previous inspections and structural analysis. This paper details the monitoring system and the analytical method for the assessment of bridge condition based on automated data-driven analyses. Two primary objectives of monitoring the system performance are discussed: 1) monitoring fatigue accumulation in critical tensile truss elements; and 2) monitoring the reliability index values associated with sub-system limit states of these members. Moreover, since the reliability index is a scalar indicator of the safety of components, quantifiable condition assessment can be used as an objective metric so that bridge owners can make informed damage mitigation strategies and optimize resource management on single bridge or network levels.

Osseointegrated prostheses which integrate the prosthesis directly to the limb bone are being developed for patients that are unable to wear traditional socket prostheses. While osseointegration of the prosthesis offers amputees improvement in their quality of life, there remains a need to better understand the integration process that occurs between the bone and the prosthesis. Quantification of the degree of integration is important to track the recuperation process of the amputee, guide physical therapy regimes, and to identify when the state of integration may change (due to damage to the bone). This study explores the development of an assessment strategy for quantitatively assessing the degree of integration between an osseointegrated prosthesis and host bone. Specifically, the strategy utilizes a titanium rod prosthesis as a waveguide with guided waves used to assess the degree of integration. By controlling waveforms launched by piezoelectric wafers bonded on the percutaneous tip of the prosthesis, body waves are introduced into the waveguide with wave reflections at the boneprosthesis interface recorded by the same array. Changes in wave energy are correlated to changes at the contact interface between the titanium rod and the bone material. Both simulation and experimental tests are presented in this paper. Experimental testing is performed using a high-density polyethylene (HDPE) host because the elastic modulus and density of HDPE are close to that of human and animal bone. Results indicate high sensitivity of the longitudinal wave energy to rod penetration depth and confinement stress issued by the host bone.

This paper describes a cloud-based cyberinfrastructure framework for the management of the diverse data involved in bridge monitoring. Bridge monitoring involves various hardware systems, software tools and laborious activities that include, for examples, a structural health monitoring (SHM), sensor network, engineering analysis programs and visual inspection. Very often, these monitoring systems, tools and activities are not coordinated, and the collected information are not shared. A well-designed integrated data management framework can support the effective use of the data and, thereby, enhance bridge management and maintenance operations. The cloud-based cyberinfrastructure framework presented herein is designed to manage not only sensor measurement data acquired from the SHM system, but also other relevant information, such as bridge engineering model and traffic videos, in an integrated manner. For the scalability and flexibility, cloud computing services and distributed database systems are employed. The information stored can be accessed through standard web interfaces. For demonstration, the cyberinfrastructure system is implemented for the monitoring of the bridges located along the I-275 Corridor in the state of Michigan.

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.

This paper describes an information repository to support bridge monitoring applications on a cloud computing platform.
Bridge monitoring, with instrumentation of sensors in particular, collects significant amount of data. In addition to
sensor data, a wide variety of information such as bridge geometry, analysis model and sensor description need to be
stored. Data management plays an important role to facilitate data utilization and data sharing. While bridge information
modeling (BrIM) technologies and standards have been proposed and they provide a means to enable integration and
facilitate interoperability, current BrIM standards support mostly the information about bridge geometry. In this study,
we extend the BrIM schema to include analysis models and sensor information. Specifically, using the OpenBrIM
standards as the base, we draw on CSI Bridge, a commercial software widely used for bridge analysis and design, and
SensorML, a standard schema for sensor definition, to define the data entities necessary for bridge monitoring
applications. NoSQL database systems are employed for data repository. Cloud service infrastructure is deployed to
enhance scalability, flexibility and accessibility of the data management system. The data model and systems are tested
using the bridge model and the sensor data collected at the Telegraph Road Bridge, Monroe, Michigan.

New advances in nanotechnology and material processing is creating opportunities for the design and fabrication of a new generation of thin film sensors that can used to assess structural health. In particular, thin film sensors attached to large areas of the structure surface has the potential to provide spatially rich data on the performance and health of a structure. This study focuses on the development of a fully integrated strain sensor that is fabricated on a flexible substrate for potentially use in sensing skins. This is completed using a carbon nanotube-polymer composite material that is patterned on a flexible polyimide substrate using optical lithography. The piezoresistive carbon nanotube elements are integrated into a complete sensing system by patterning copper electrodes and integrating off-the-shelf electrical components on the flexible film for expanded functionality. This diverse material utilization is realized in a versatile process flow to illustrate a powerful toolbox for sensing severity, location, and failure mode of damage on structural components. The fully integrated patterned carbon nanotube strain sensor is tested on a quarter-scale, composite beam column connection. The results and implications for future structural damage detection are discussed.

Unmanned aerial vehicles (UAVs) can serve as a powerful mobile sensing platform for assessing the health of civil infrastructure systems. To date, the majority of their uses have been dedicated to vision and laser-based spatial imaging using on-board cameras and LiDAR units, respectively. Comparatively less work has focused on integration of other sensing modalities relevant to structural monitoring applications. The overarching goal of this study is to explore the ability for UAVs to deploy a network of wireless sensors on structures for controlled vibration testing. The study develops a UAV platform with an integrated robotic gripper that can be used to install wireless sensors in structures, drop a heavy weight for the introduction of impact loads, and to uninstall wireless sensors for reinstallation elsewhere. A pose estimation algorithm is embedded in the UAV to estimate the location of the UAV during sensor placement and impact load introduction. The Martlet wireless sensor network architecture is integrated with the UAV to provide the UAV a mobile sensing capability. The UAV is programmed to command field deployed Martlets, aggregate and temporarily store data from the wireless sensor network, and to communicate data to a fixed base station on site. This study demonstrates the integrated UAV system using a simply supported beam in the lab with Martlet wireless sensors placed by the UAV and impact load testing performed. The study verifies the feasibility of the integrated UAV-wireless monitoring system architecture with accurate modal characteristics of the beam estimated by modal analysis.

The detection of damage in structures at its earliest stages has many economical and safety benefits. Permanent monitoring systems using various forms of sensor networks and analysis methods are often employed to increase the frequency and diagnostic capabilities of inspections. Some of these techniques provide spatial/volumetric information about a given area/volume of a structure. Many of the available spatial sensing techniques can be costly and cannot be permanently deployed (e.g., IR camera thermography). For this reason intricate analysis methods using permanently deployable sensors are being developed (e.g., ultrasonic piezoelectrics, sensing skins). One approach is to leverage the low cost of heaters and temperature sensors to develop an economical, permanently installable method of spatial damage detection using heat transfer. This paper presents a method similar to that of X-ray computed tomography (CT). However, the theories for Xray CT must be adapted to properly represent heat transfer as well as account for the relatively large and immobile sensors spacing used on a structure (i.e., there is a finite number of heaters/sensors permanently installed around the perimeter of the area of interest). The derivation of heat transfer computed tomography is discussed in this paper including two methods for steering the effective heat wave. A high fidelity finite element method (FEM) model is used to verify the analytical derivation of individual steps within the method as well as simulate the complete damage detection technique. Experimental results from both damaged and undamaged aluminum plate specimens are used to validate the FEM model and to justify theoretical assumptions. The simulation results are discussed along with possible improvements and modifications to the technique.

Advances in wireless sensor technology have enabled low cost and extremely scalable sensing platforms prompting high density sensor installations. High density long-term monitoring generates a wealth of sensor data demanding an efficient means of data storage and data processing for information extraction that is pertinent to the decision making of bridge owners. This paper reports on decision making inferences drawn from automated data processing of long-term highway bridge data. The Telegraph Road Bridge (TRB) demonstration testbed for sensor technology innovation and data processing tool development has been instrumented with a long-term wireless structural monitoring system that has been in operation since September 2011. The monitoring system has been designed to specifically address stated concerns by the Michigan Department of Transportation regarding pin and hanger steel girder bridges. The sensing strategy consists of strain, acceleration and temperature sensors deployed in a manner to track specific damage modalities common to multigirder steel concrete composite bridges using link plate assemblies. To efficiently store and process long-term sensor data, the TRB monitoring system operates around the SenStore database system. SenStore combines sensor data with bridge information (e.g., material properties, geometry, boundary conditions) and exposes an application programming interface to enable automated data extraction by processing tools. Large long-term data sets are modeled for environmental and operational influence by regression methods. Response processes are defined by statistical parameters extracted from long-term data and used to automate decision support in an outlier detection, or statistical process control, framework.

The introduction of wireless telemetry into the design of monitoring and control systems has been shown to reduce
system costs while simplifying installations. To date, wireless nodes proposed for sensing and actuation in cyberphysical
systems have been designed using microcontrollers with one computational pipeline (i.e., single-core
microcontrollers). While concurrent code execution can be implemented on single-core microcontrollers,
concurrency is emulated by splitting the pipeline’s resources to support multiple threads of code execution. For
many applications, this approach to multi-threading is acceptable in terms of speed and function. However, some
applications such as feedback controls demand deterministic timing of code execution and maximum computational
throughput. For these applications, the adoption of multi-core processor architectures represents one effective
solution. Multi-core microcontrollers have multiple computational pipelines that can execute embedded code in
parallel and can be interrupted independent of one another. In this study, a new wireless platform named Martlet is
introduced with a dual-core microcontroller adopted in its design. The dual-core microcontroller design allows
Martlet to dedicate one core to standard wireless sensor operations while the other core is reserved for embedded
data processing and real-time feedback control law execution. Another distinct feature of Martlet is a standardized
hardware interface that allows specialized daughter boards (termed wing boards) to be interfaced to the Martlet
baseboard. This extensibility opens opportunity to encapsulate specialized sensing and actuation functions in a wing
board without altering the design of Martlet. In addition to describing the design of Martlet, a few example wings
are detailed, along with experiments showing the Martlet’s ability to monitor and control physical systems such as
wind turbines and buildings.

The technical challenges of managing the health of critical infrastructure systems necessitate greater structural sensing capabilities. Among these needs is the ability for quantitative, spatial damage detection on critical structural components. Advances in material science have now opened the door for novel and cost-effective spatial sensing solutions specially tailored for damage detection in structures. However, challenges remain before spatial damage detection can be realized. Some of the technical challenges include sensor installations and extensive signal processing requirements. This work addresses these challenges by developing a patterned carbon nanotube composite thin film sensor whose pattern has been optimized for measuring the spatial distribution of strain. The carbon nanotube-polymer nanocomposite sensing material is fabricated on a flexible polyimide substrate using a layer-by-layer deposition process. The thin film sensors are then patterned into sensing elements using optical lithography processes common to microelectromechanical systems (MEMS) technologies. The sensor array is designed as a series of sensing elements with varying width to provide insight on the limitations of such patterning and implications of pattern geometry on sensing signals. Once fabrication is complete, the substrate and attached sensor are epoxy bonded to a poly vinyl composite (PVC) bar that is then tested with a uniaxial, cyclic load pattern and mechanical response is characterized. The fabrication processes are then utilized on a larger-scale to develop and instrument a component-specific sensing skin in order to observe the strain distribution on the web of a steel beam. The instrumented beam is part of a larger steel beam-column connection with a concrete slab in composite action. The beam-column subassembly is laterally loaded and strain trends in the web are observed using the carbon nanotube composite sensing skin. The results are discussed in the context of understanding the properties of the thin film sensor and how it may be advanced toward structural sensing applications.

A worthy goal for the structural health monitoring field is the creation of a scalable monitoring system architecture that abstracts many of the system details (e.g., sensors, data) from the structure owner with the aim of providing “actionable” information that aids in their decision making process. While a broad array of sensor technologies have emerged, the ability for sensing systems to generate large amounts of data have far outpaced advances in data management and processing. To reverse this trend, this study explores the creation of a cyber-enabled wireless SHM system for highway bridges. The system is designed from the top down by considering the damage mechanisms of concern to bridge owners and then tailoring the sensing and decision support system around those concerns. The enabling element of the proposed system is a powerful data repository system termed SenStore. SenStore is designed to combine sensor data with bridge meta-data (e.g., geometric configuration, material properties, maintenance history, sensor locations, sensor types, inspection history). A wireless sensor network deployed to a bridge autonomously streams its measurement data to SenStore via a 3G cellular connection for storage. SenStore securely exposes the bridge meta- and sensor data to software clients that can process the data to extract information relevant to the decision making process of the bridge owner. To validate the proposed cyber-enable SHM system, the system is implemented on the Telegraph Road Bridge (Monroe, MI). The Telegraph Road Bridge is a traditional steel girder-concrete deck composite bridge located along a heavily travelled corridor in the Detroit metropolitan area. A permanent wireless sensor network has been installed to measure bridge accelerations, strains and temperatures. System identification and damage detection algorithms are created to automatically mine bridge response data stored in SenStore over an 18-month period. Tools like Gaussian Process (GP) regression are used to predict changes in the bridge behavior as a function of environmental parameters. Based on these analyses, pertinent behavioral information relevant to bridge management is autonomously extracted.

This study investigated a number of different damage detection algorithms for structural health monitoring of a typical suspension bridge. The Alfred Zampa Memorial Bridge, a part of the Interstate 80 in California, was selected for this study. The focus was to implement and validate simple damage detection algorithms for structural health monitoring of complex bridges. Accordingly, the numerical analysis involved development of a high fidelity finite element model of the bridge in order to simulate various structural damage scenarios. The finite element model of the bridge was validated based on the experimental modal properties. A number of damage scenarios were simulated by changing the stiffness of different bridge components including suspenders, main cable, bulkheads and deck. Several vibration-based damage detection methods namely the change in the stiffness, change in the flexibility, change in the uniform load surface and change in the uniform load surface curvature were employed to locate the simulated damages. The investigation here provides the relative merits and shortcomings of these methods when applied to long span suspension bridges. It also shows the applicability of these methods to locate the decay in the structure.

This study introduces an efficient procedure to estimate the structural response of a suspension bridge in real-time based on a limited set of measured data. Unlike conventional techniques, the proposed procedure does not employ mode shapes and frequencies. In this study, the proposed technique is used to estimate the response of a suspension bridge structure based on a set of strain gauge measurements. Finite element analysis is performed only once to set up the structural parameters, namely computed flexibility matrix, and computed hanger forces matrix. The response of the bridge was estimated without any additional finite element analysis using the computed structural parameters and the measured hanger strains. The Alfred Zampa Memorial Bridge, on Interstate 80 in California, was selected for this study. A high fidelity finite element model of the bridge was developed using the general purpose computer program ADINA. The proposed method has been proven to have the capability to estimate any type of structural response in real time based on the measured hanger strains, and provides an important part of an integrated Structure Health Monitoring (SHM) system for major bridges.

While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.

Damage detection on engineered systems is a challenging task that has been explored by numerous researchers. In recent years wireless sensors systems have arisen as a vehicle for low-power, low-cost, and localized damage detection that can be applied to various structural systems. Such sensors, however, are limited in their computational capacity and as a result, careful consideration must be taken as to which algorithms can be effectively embedded so as to balance energy constraints with computational efficiency. In this study, two classifier algorithms (least squares classifier and Fisher's linear discriminant analysis) are explored for detecting damage on a cooling system test bed. In particular, the algorithms are used to determine the valve configuration of the system and to verify if damage exists within the valves. To validate the efficiency of the algorithms in the embedded domain, the algorithms are implemented on a wireless sensing network and used to classify the system state of the test bed.

Structural Health Monitoring of large-scale bridges requires collection, storage and processing of large amounts of data, and must provide distributed concurrent access. In this paper we report on the progress of the design and implementation of a cyber-infrastructure system that is currently being field-tested on a long-span bridge in California and a short-span bridge in Michigan. This system provides remote access for sensor data acquisition systems, data analysis modules, and human operators. The implementation is based on an object-oriented data model description and makes extensive use of code generation to allow for the rapid development and continued improvement of the system. Currently the system provides storage of raw and processed sensor data, finite element models, traffic data, links to PONTIS data, reliability modeling data, and model-based analysis results. Apart from the ubiquitous read/write access, the system also includes an event system that allows data consumers to be triggered by the arrival of new data. In addition to essential backup/restore facilities, the system also includes import/export tools that can migrate data between versions, which is very useful in keeping pace with the continuous improvements that are being made in the design and implementation of the cyber-infrastructure system. The system also provides introspection, as the data model is made available by means of an inspector client interface, which allows the development of generic client tools that can dynamically discover the data model, and present a corresponding interface to the user. Currently available user-interfaces include an editor GUI application, and a read-only web application.

Wireless sensors have emerged to offer low-cost sensors with impressive functionality (e.g., data acquisition, computing, and communication) and modular installations. Such advantages enable higher nodal densities than tethered systems resulting in increased spatial resolution of the monitoring system. However, high nodal density comes at a cost as huge amounts of data are generated, weighing heavy on power sources, transmission bandwidth, and data management requirements, often making data compression necessary. The traditional compression paradigm consists of high rate (>Nyquist) uniform sampling and storage of the entire target signal followed by some desired compression scheme prior to transmission. The recently proposed compressed sensing (CS) framework combines the acquisition and compression stage together, thus removing the need for storage and operation of the full target signal prior to transmission. The effectiveness of the CS approach hinges on the presence of a sparse representation of the target signal in a known basis, similarly exploited by several traditional compressive sensing applications today (e.g., imaging, MRI). Field implementations of CS schemes in wireless SHM systems have been challenging due to the lack of commercially available sensing units capable of sampling methods (e.g., random) consistent with the compressed sensing framework, often moving evaluation of CS techniques to simulation and post-processing. The research presented here describes implementation of a CS sampling scheme to the Narada wireless sensing node and the energy efficiencies observed in the deployed sensors. Of interest in this study is the compressibility of acceleration response signals collected from a multi-girder steel-concrete composite bridge. The study shows the benefit of CS in reducing data requirements while ensuring data analysis on compressed data remain accurate.

Vehicle/structure interaction is extremely important in determining the structural performance of highway bridges.
However, an accurate prediction of the generated vibrations and forces requires a high-fidelity nonlinear 3D model
which is sufficiently representative of the actual vehicle and bridge structure. In spite of all the computational
advancements, there are still many technical difficulties to obtain a converging solution from a coupled highly nonlinear
and highly damped vehicle/structure models. This paper presents an iterative uncoupled approach to obtain an accurate
estimation of the vehicle/structure interaction. The multi-axle vehicle is simulated using a nonlinear 3D multibody
dynamics model. The bridge model also contains several nonlinear components to accurately model the bridge behavior.
The vehicle/bridge interaction results are obtained through an iterative solution by exchanging the outputs of two
uncoupled nonlinear models. A convergence criterion is selected to obtain a reliable solution after several of these
iterations. Finally, a reduced-order model of the bridge is developed using a state-space model. The linear reduced-order
model of the bridge is coupled with the nonlinear vehicle model to improve the solution time of the analysis. The results
are in a very good agreement with the iterative uncoupled approach.

This paper presents the implementation of the Finite Element (FE) model updating for a skewed highway bridge using
real-time sensor data. The bridge under investigation is a I-275 crossing in Wayne County, Michigan. The bridge is
instrumented with a wireless sensory system to collect the vibration response of the bridge under ambient vibrations. The
dynamic characteristics of the bridge have been studied through the field measurements as well as a high-fidelity FE
model of the bridge. The developed finite element model of the bridge is updated with the field measured response of the
bridge so that the FE computed and field measured modal characteristics of the bridge match each other closely. A
comprehensive sensitivity analysis was performed to determine the structural parameters of the FE model which affect
the modal frequencies and modal shapes the most. A multivariable sensitivity-based objective function is constructed to
minimize the error between the experimentally measured and the FE predicted modal characteristics. The selected
objective function includes information about both modal frequencies and mode shapes of the bridge. An iterative
approach has been undertaken to find the optimized structural parameters of the FE model which minimizes the selected
objective function. Appropriate constraints and boundary conditions are used during the optimization process to prevent
non-physical solutions. The final updated FE model of the bridge provides modal results which are very consistent with
the experimentally measured modal characteristics.

The emergence of cost-effective sensing technologies has now enabled the use of dense arrays of sensors to monitor the
behavior and condition of large-scale bridges. The continuous operation of dense networks of sensors presents a number
of new challenges including how to manage such massive amounts of data that can be created by the system. This paper
reports on the progress of the creation of cyberinfrastructure tools which hierarchically control networks of wireless
sensors deployed in a long-span bridge. The internet-enabled cyberinfrastructure is centrally managed by a powerful
database which controls the flow of data in the entire monitoring system architecture. A client-server model built upon
the database provides both data-provider and system end-users with secured access to various levels of information of a
bridge. In the system, information on bridge behavior (e.g., acceleration, strain, displacement) and environmental
condition (e.g., wind speed, wind direction, temperature, humidity) are uploaded to the database from sensor networks
installed in the bridge. Then, data interrogation services interface with the database via client APIs to autonomously
process data. The current research effort focuses on an assessment of the scalability and long-term robustness of the
proposed cyberinfrastructure framework that has been implemented along with a permanent wireless monitoring system
on the New Carquinez (Alfred Zampa Memorial) Suspension Bridge in Vallejo, CA. Many data interrogation tools are
under development using sensor data and bridge metadata (e.g., geometric details, material properties, etc.) Sample data
interrogation clients including those for the detection of faulty sensors, automated modal parameter extraction.

Embedded computation in wireless sensor networks (WSN) can extract useful information from sensor data in a fast and
efficient manner. Embedded computing has the benefit of saving both bandwidth and power. However, computational
capability often comes at the expense of power consumption on the wireless sensor node. This is an especially critical
issue for battery powered wireless sensor nodes. By developing a hybrid network consisting of wireless units optimized
for sensing interspersed with more powerful computationally focused units, it is now possible to build a network that is
more efficient and flexible than a homogeneous WSN. For this project, such a network was developed using Narada
units as low-power sensing units and iMote2 units as ultra-efficient computational engines. In order to demonstrate the
capabilities of such a configuration a network was created to extract structural modal parameters based on Markov
parameters. This paper validates the performance of the heterogeneous WSN using a laboratory structure tested under
impulse loading.

A dense network of sensors installed in a bridge can continuously generate response data from which the health and
condition of the bridge can be analyzed. This approach to structural health monitoring the efforts associated with
periodic bridge inspections and can provide timely insight to regions of the bridge suspected of degradation or damage.
Nevertheless, the deployment of fine sensor grids on large-scale structures is not feasible using wired monitoring
systems because of the rapidly increasing installation labor and costs required. Moreover, the enormous size of raw
sensor data, if not translated into meaningful forms of information, can paralyze the bridge manager's decision making.
This paper reports the development of a large-scale wireless structural monitoring system for long-span bridges; the
system is entirely wireless which renders it low-cost and easy to install. Unlike central tethered data acquisition systems
where data processing occurs in the central server, the distributed network of wireless sensors supports data processing.
In-network data processing reduces raw data streams into actionable information of immediate value to the bridge
manager. The proposed wireless monitoring system has been deployed on the New Carquinez Suspension Bridge in
California. Current efforts on the bridge site include: 1) long-term assessment of a dense wireless sensor network; 2)
implementation of a sustainable power management solution using solar power; 3) performance evaluation of an
internet-enabled cyber-environment; 4) system identification of the bridge; and 5) the development of data mining tools.
A hierarchical cyber-environment supports peer-to-peer communication between wireless sensors deployed on the
bridge and allows for the connection between sensors and remote database systems via the internet. At the remote
server, model calibration and damage detection analyses that employ a reduced-order finite element bridge model are
implemented.

In this paper, the development of a new variation of Engineered Cementitious Composite (ECC) that aims to combine
tensile ductility with self-sensing ability is described. ECC is a new type of high-performance fiber reinforced
cementitious composite that exhibits strain-hardening under applied tensile load while resisting fracture localization. The
self-sensing ability is achieved by incorporating a small dosage of carbon black (CB) into the ECC system (hereafter
known as CB-ECC) to enhance its piezoresistive behavior while maintaining its tensile strain-hardening behavior. The
tensile stress-strain response of CB-ECC is studied with an emphasis on its tensile stress and strain capacity, as well as
its cracking pattern. In addition, the piezoresistive behavior of CB-ECC under uniaxial tension is investigated.
Specifically, the effect of carbon black content on the electrical properties of ECC including the sensitivity of changes in
its bulk conductivity under applied tensile strain are explored in detail.

A chloride ion selective thin film sensor is proposed for measuring chloride ion concentration, which is an
environmental parameter correlated to corrosion. In this work, electrochemical polymerization of Polypyrrole (PPy)
doped with chloride ions was achieved on the top of a carbon nanotube (CNT) thin film as a working electrode in an
electrochemical cell. The underlying CNT layer conjugated with doped PPy thin film can form a multifunctional "selfsensing"
material platform for chloride ion detection in a concrete environment. The paper presents the first type of work
using CNT and PPy as hybrid materials for chloride ion sensing. Electrochemical polymerization of PPy results in
oxidation that yields an average of one positive charge distributed over four pyrrole units. This positive charge is
compensated by negatively-charged chloride ions in the supporting electrolyte. In effect, the chloride ion-doped PPy has
become molecularly imprinted with chloride ions thereby providing it with some degree of perm-selectivity for chloride
ions. The detection limit of the fabricated chloride ion-doped PPy thin film can reach 10-8 M and selectivity coefficients
are comparable to those in the literature. The reported work aims to lay a strong foundation for detecting chloride ion
concentrations in the concrete environment.

Wireless sensing technologies have recently emerged as an inexpensive and robust method of data collection in a variety
of structural monitoring applications. In comparison with cabled monitoring systems, wireless systems offer low-cost
and low-power communication between a network of sensing devices. Wireless sensing networks possess embedded
data processing capabilities which allow for data processing directly at the sensor, thereby eliminating the need for the
transmission of raw data. In this study, the Volterra/Weiner neural network (VWNN), a powerful modeling tool for nonlinear
hysteretic behavior, is decentralized for embedment in a network of wireless sensors so as to take advantage of
each sensor's processing capabilities. The VWNN was chosen for modeling nonlinear dynamic systems because its
architecture is computationally efficient and allows computational tasks to be decomposed for parallel execution. In the
algorithm, each sensor collects it own data and performs a series of calculations. It then shares its resulting calculations
with every other sensor in the network, while the other sensors are simultaneously exchanging their information.
Because resource conservation is important in embedded sensor design, the data is pruned wherever possible to eliminate
excessive communication between sensors. Once a sensor has its required data, it continues its calculations and computes
a prediction of the system acceleration. The VWNN is embedded in the computational core of the Narada wireless
sensor node for on-line execution. Data generated by a steel framed structure excited by seismic ground motions is used
for validation of the embedded VWNN model.

Bridges undergo dynamic vehicle-bridge interaction when heavy vehicles drive over them at high speeds. Traditionally,
analytical models representing the dynamics of the bridge and vehicle have been utilized to understand the complex
vehicle-bridge interaction. Analytical approaches have dominated the field due to the numerous challenges associated
with field testing. Foremost among the challenges is the cost and difficulties associated with the measurement of two
different systems, i.e. mobile vehicle and static bridge. The recent emergence of wireless sensors in the field of
structural monitoring has created an opportunity to directly monitor the vehicle-bridge interaction. In this study, the
unrestricted mobility of wireless sensors is utilized to monitor the dynamics of test vehicle driving over a bridge. The
integration of the mobile wireless sensor network in the vehicle with a static wireless monitoring system installed in the
bridge provides a time-synchronized data set from which vehicle-bridge interaction can be studied. A network of
Narada wireless sensor nodes are installed in a test truck to measure vertical vibrations, rotational pitching, and
horizontal acceleration. A complementary Narada wireless sensor network is installed on the Geumdang Bridge
(Icheon, Korea) to measure the vertical acceleration response of the bridge under the influence of the truck. The
horizontal acceleration of the vehicle is used to estimate the position trajectory of the truck on the bridge using Kalman
filtering techniques. Experimental results reveal accurate truck position estimation and highly reliable wireless data
collection from both the vehicle and the bridge.

Concrete pipelines are one of the most popular underground lifelines used for the transportation of water resources.
Unfortunately, this critical infrastructure system remains vulnerable to ground displacements during seismic and
landslide events. Ground displacements may induce significant bending, shear, and axial forces to concrete pipelines
and eventually lead to joint failures. In order to understand and model the typical failure mechanisms of concrete
segmented pipelines, large-scale experimentation is necessary to explore structural and soil-structure behavior during
ground faulting. This paper reports on the experimentation of a reinforced concrete segmented concrete pipeline using
the unique capabilities of the NEES Lifeline Experimental and Testing Facilities at Cornell University. Five segments of
a full-scale commercial concrete pressure pipe (244 cm long and 37.5 cm diameter) are constructed as a segmented
pipeline under a compacted granular soil in the facility test basin (13.4 m long and 3.6 m wide). Ground displacements
are simulated through translation of half of the test basin. A dense array of sensors including LVDT's, strain gages, and
load cells are installed along the length of the pipeline to measure the pipeline response while the ground is incrementally displaced. Accurate measures of pipeline displacements and strains are captured up to the compressive and flexural failure of the pipeline joints.

Bridges are an important societal resource used to carry vehicular traffic within a transportation network. As such, the
economic impact of the failure of a bridge is high; the recent failure of the I-35W Bridge in Minnesota (2007) serves as a
poignant example. Structural health monitoring (SHM) systems can be adopted to detect and quantify structural
degradation and damage in an affordable and real-time manner. This paper presents a detailed overview of a multi-tiered
architecture for the design of a low power wireless monitoring system for large and complex infrastructure systems. The
monitoring system architecture employs two wireless sensor nodes, each with unique functional features and varying
power demand. At the lowest tier of the system architecture is the ultra-low power Phoenix wireless sensor node whose
design has been optimized to draw minimal power during standby. These ultra low-power nodes are configured to
communicate their measurements to a more functionally-rich wireless sensor node residing on the second-tier of the
monitoring system architecture. While the Narada wireless sensor node offers more memory, greater processing power
and longer communication ranges, it also consumes more power during operation. Radio frequency (RF) and mechanical vibration power harvesting is integrated with the wireless sensor nodes to allow them to operate freely for long periods of time (e.g., years). Elements of the proposed two-tiered monitoring system architecture are validated upon an operational long-span suspension bridge.

A smart antenna has been developed for structural health monitoring. The antenna is based on Monarch's GEN 2 selfstructuring
antenna (SSA) technology and provides polarization and beam-diversity for improving signal-to-noise ratio
(SNR). The antenna works with University of Michigan's Narada platform, where a microcontroller monitors the RSSI
and selects the best beam to maintain reliable RF link. Antenna has two wide beams for each polarization and the beams
are selected by applying appropriate DC voltages to the RF switches on the antenna aperture. Paper presents the GEN C antenna, which is a smaller version of the GEN 2B with comparable performance features.

In this paper we focus on the optimal placement of sensors for state estimation-based continuous health monitoring
of structures using three approaches. The first aims to minimize the static estimation error of the structure
deflections, using the linear stiffness matrix derived from a finite element model. The second approach aims to
maximize the observability of the derived linear state space model. The third approach aims to minimize the dynamic estimation error of the deflections using a Linear Quadratic Estimator. Both nonlinear mixed-integer and relaxed convex optimization formulations are presented. A simple search-based optimization implementation for each of the three approaches is demonstrated on a model of the long-span New Carquinez Bridge in California.

This paper presents an interim report on an international collaborative research project between the United States and
Korea that fundamentally addresses the challenges associated with integrating structural health monitoring (SHM)
system components into a comprehensive system for bridges. The objective of the project is to integrate and validate
cutting-edge sensors and SHM methods under development for monitoring the long-term performance and structural
integrity of highway bridges. A variety of new sensor and monitoring technologies have been selected for integration
including wireless sensors, EM stress sensors and piezoelectric active sensors. Using these sensors as building blocks,
the first phase of the study focuses on the design of a comprehensive SHM system that is deployed upon a series of
highway bridges in Korea. With permanently installed SHM systems in place, the second phase of the study provides
open access to the bridges and response data continuously collected as an internal test-bed for SHM. Currently, basic
facilities including Internet lines have been constructed on the test-beds, and the participants carried out tests on bridges
on the test road section owned by the Korea Expressway Corporation (KEC) with their own measurement and
monitoring systems in the local area network environment. The participants were able to access and control their
measurement systems by using Remote Desktop in Windows XP through Internet. Researchers interested in this test-bed
are encouraged to join in the collaborative research.

Recent advances in wireless sensing technology have made it possible to deploy dense networks of sensing transducers
within large structural systems. Because these networks leverage the embedded computing power and agent-based
abilities integral to many wireless sensing devices, it is possible to analyze sensor data autonomously and in-network. In
this study, market-based techniques are used to autonomously estimate mode shapes within a network of agent-based
wireless sensors. Specifically, recent work in both decentralized Frequency Domain Decomposition and market-based
resource allocation is leveraged to create a mode shape estimation algorithm derived from free-market principles. This
algorithm allows an agent-based wireless sensor network to autonomously shift emphasis between improving mode
shape accuracy and limiting the consumption of certain scarce network resources: processing time, storage capacity, and
power consumption. The developed algorithm is validated by successfully estimating mode shapes using a network of
wireless sensor prototypes deployed on the mezzanine balcony of Hill Auditorium, located on the University of
Michigan campus.

The continued development of renewable energy resources is for the nation to limit its carbon footprint and to enjoy
independence in energy production. Key to that effort are reliable generators of renewable energy sources that are
economically competitive with legacy sources. In the area of wind energy, a major contributor to the cost of
implementation is large uncertainty regarding the condition of wind turbines in the field due to lack of information about
loading, dynamic response, and fatigue life of the structure expended. Under favorable circumstances, this uncertainty
leads to overly conservative designs and maintenance schedules. Under unfavorable circumstances, it leads to
inadequate maintenance schedules, damage to electrical systems, or even structural failure. Low-cost wireless sensors
can provide more certainty for stakeholders by measuring the dynamic response of the structure to loading, estimating
the fatigue state of the structure, and extracting loading information from the structural response without the need of an
upwind instrumentation tower. This study presents a method for using wireless sensor networks to estimate the spectral
properties of a wind turbine tower loading based on its measured response and some rudimentary knowledge of its
structure. Structural parameters are estimated via model-updating in the frequency domain to produce an identification
of the system. The updated structural model and the measured output spectra are then used to estimate the input spectra.
Laboratory results are presented indicating accurate load characterization.

Seismic damage to buried pipelines is mainly caused by permanent ground displacements, typically concentrated in the
vicinity of the fault line in the soil. In particular, a pipeline crossing the fault plane is subjected to significant bending,
shear, and axial forces. While researchers have explored the behavior of segmented metallic pipelines under permanent
ground displacement, comparatively less experimental work has been conducted on the performance of segmented
concrete pipelines. In this study, a large-scale test is conducted on a segmented concrete pipeline using the unique
capabilities of the NEES Lifeline Experimental and Testing Facilities at Cornell University. A total of 13 partial-scale
concrete pressure pipes (19 cm diameter and 86 cm long) are assembled into a continuous pipeline and buried in a loose
granular soil. Permanent ground displacement that places the segmented concrete pipeline in compression is simulated
through the translation of half of the soil test basin. A dense array of sensors including linear variable differential
transducers, strain gauges, and load cells are installed along the length of the pipeline to measure its response to ground
displacement. Response data collected from the pipe suggests that significant damage localization occurs at the ends of
the segment crossing the fault plane, resulting in rapid catastrophic failure of the pipeline.

Despite the wide variety of effective disinfection and wastewater treatment techniques for removing organic and
inorganic wastes, pollutants such as nitrogen remain in wastewater effluents. If left untreated, these nitrogenous wastes
can adversely impact the environment by promoting the overgrowth of aquatic plants, depleting dissolved oxygen, and
causing eutrophication. Although nitrification/denitrification processes are employed during advanced wastewater
treatment, effective and efficient operation of these facilities require information of the pH, dissolved oxygen content,
among many other parameters, of the wastewater effluent. In this preliminary study, a biocompatible CNT-based
nanocomposite is proposed and validated for monitoring the biological metabolic activity of nitrifying bacteria in
wastewater effluent environments (i.e., to monitor the nitrification process). Using carbon nanotubes and a pH-sensitive
conductive polymer (i.e., poly(aniline) emeraldine base), a
layer-by-layer fabrication technique is employed to fabricate
a novel thin film pH sensor that changes its electrical properties in response to variations in ambient pH environments.
Laboratory studies are conducted to evaluate the proposed nanocomposite's biocompatibility with wastewater effluent
environments and its pH sensing performance.

The long-term deterioration of large-scale infrastructure systems is a critical national problem that if left unchecked,
could lead to catastrophes similar in magnitude to the collapse of the I-35W Bridge. Fortunately, the past decade has
witnessed the emergence of a variety of sensing technologies from many engineering disciplines including from the
civil, mechanical and electrical engineering fields. This paper provides a detailed overview of an emerging set of sensor
technologies that can be effectively used for health management of large-scale infrastructure systems. In particular, the
novel sensing technologies are integrated to offer a comprehensive monitoring system that fundamentally addresses the
limitations associated with current monitoring systems (for example, indirect damage sensing, cost, data inundation and
lack of decision making tools). Self-sensing materials are proposed for distributed, direct sensing of specific damage
events common to civil structures such as cracking and corrosion. Data from self-sensing materials, as well as from
more traditional sensors, are collected using ultra low-power wireless sensors powered by a variety of power harvesting
devices fabricated using microelectromechanical systems (MEMS). Data collected by the wireless sensors is then
seamlessly streamed across the internet and integrated with a database upon which finite element models can be
autonomously updated. Life-cycle and damage detection analyses using sensor and processed data are streamed into a
decision toolbox which will aid infrastructure owners in their decision making.

The installation of a structural monitoring system on a medium- to large-span bridge can be a challenging undertaking
due to high system costs and time consuming installations. However, these historical challenges can be eliminated by
using wireless sensors as the primary building block of a structural monitoring system. Wireless sensors are low-cost
data acquisition nodes that utilize wireless communication to transfer data from the sensor to the data repository.
Another advantageous characteristic of wireless sensors is their ability to be easily removed and reinstalled in another
sensor location on the same structure; this installation modularity is highlighted in this study. Wireless sensor nodes
designed for structural monitoring applications are installed on the 180 m long Yeondae Bridge (Korea) to measure the
dynamic response of the bridge to controlled truck loading. To attain a high nodal density with a small number (20) of
wireless sensors, the wireless sensor network is installed three times with each installation concentrating sensors in one
portion of the bridge. Using forced and free vibration response data from the three installations, the modal properties of
the bridge are accurately identified. Intentional nodal overlapping of the three different sensor installations allows mode
shapes from each installation to be stitched together into global mode shapes. Specifically, modal properties of the
Yeondae Bridge are derived off-line using frequency domain decomposition (FDD) modal analysis methods.

Recent years have seen growing interest in applying wireless sensing and embedded computing technologies for
structural health monitoring and control. The incorporation of these new technologies greatly reduces system cost by
eliminating expensive lengthy cables, and enables highly flexible system architectures. Previous research has
demonstrated the feasibility of decentralized wireless structural control through numerical simulations and preliminary
laboratory experiments with a three-story structure. This paper describes latest laboratory experiments that are designed
to further evaluate the performance of decentralized wireless structural control using a six-story structure. Commanded
by wireless sensors and controllers, semi-active magnetorheological (MR) dampers are installed between neighboring
floors for applying real-time feedback control forces. Multiple centralized/decentralized feedback control architectures
have been investigated in the experiments, in combination with different sampling frequencies. The experiments offer
valuable insight in applying decentralized wireless control to larger-scale civil structures.

Piezoelectric materials have received considerable attention from the smart structure community because of their potential use as sensors, actuators and power harvesters. In particular, polyvinylidene fluoride (PVDF) has been proposed in recent years as an enabling material for a variety of sensing and energy harvesting applications. In this study, carbon nanotubes (CNT) are included within a PVDF matrix to enhance the properties of PVDF. The CNT-PVDF composite is fabricated by solvent evaporation and melt pressing. The inclusion of CNT allows the dielectric properties of the PVDF material to be adjusted such that lower poling voltages can be used to induce a permanent piezoelectric effect in the composite. To compare the piezoelectric characteristics of the CNT-PVDF composite proposed, scanning electron microscope (SEM) images were analyzed and ferroelectric experiments were conducted. Finally, the aforementioned composites were mounted upon the surface of a cantilevered beam to compare the voltage generation of the CNT-PVDF composite against homogeneous PVDF thin films.

The integrity and safety of metallic structures can be jeopardized by structural damage (e.g., yielding, cracking, impact, corrosion) that can occur during operation or service. While a variety of sensors have been proposed and validated for structural health monitoring, most sensors only provide data regarding a discrete point on the structure, thereby requiring densely-distributed sensors; however, such an approach may be infeasible for many structures due to geometrical and economic constraints. In this study, a nanoengineered carbon nanotube-polyelectrolyte sensing skin is proposed for monitoring strain, impact, and corrosion of metallic structures. Experimental validation studies have verified that these conformable films exhibit highly sensitive electromechanical and electrochemical responses to applied strain and corrosion processes, respectively. Here, the proposed nanocomposite is coupled with an electrical impedance tomographic (EIT) conductivity imaging technique. Unlike traditional point-based sensing transducers, EIT reconstructs two-dimensional skin conductivity distributions for damage identification of large structural components. Since EIT relies solely on boundary electrical measurements, one does not need to physically probe each structural location for data acquisition. Instead, any structural damage that affects the nanocomposite coating will produce localized changes in film conductivity detectable via EIT and boundary electrical measurements.

As costs associated with wireless sensing technologies continue to decline, it has become feasible to deploy dense
networks of tens, if not hundreds of wireless sensors within a single structural system. Additionally, many state-of-the-art
wireless sensing platforms now integrate low-power microprocessors and high-precision analog-to-digital converters
in their designs. As a result, data processing tasks can be efficiently distributed across large networks of wireless sensors.
In this study, a parallelized model updating algorithm is designed for implementation within a network of wireless
sensing prototypes. Using a novel parallel simulated annealing search method optimized for in-network execution, this
algorithm efficiently assigns model parameters so as to minimize differences between an analytical model of the
structure and wirelessly collected sensor data. Validation of this approach is provided by updating a lumped-mass shear
structure model of a six-story steel building exposed to seismic base motion.

A structural control system consists of sensors, controllers, and actuators integrated in a single network to effectively
mitigate building vibration during external excitations. The costs associated with high-capacity actuators and system
installation are factors impeding the wide spread adoption of structural control technology. Wireless communication can
potentially lower installation costs by eliminating coaxial cables and offer better flexibility and adaptability in the design
of a structural control system. This paper introduces a prototype wireless sensing and control unit that can be
incorporated in a real-time structural control system. Tests are conducted using a 3-story half-scale laboratory structure
instrumented with magnetorheological dampers to validate the feasibility of the wireless structural control system. This
paper also addresses the serious issue of time delay and communication range inherent to wireless technologies.
Numerical simulations using different decentralized structural control strategies are conducted on a 20-story steel
structure controlled by semi-active hydraulic dampers.

The field of nanotechnology is rapidly maturing into a fertile and interdisciplinary research area from which new sensor
and actuator technologies can be conceived. The tools and processes derived from the nanotechnology field have
offered engineers the opportunity to design materials in which sensing transduction mechanisms can be intentionally
encoded. For example, single- and multi-walled carbon nanotubes embedded within polyelectrolyte thin films have been
proposed for strain and pH sensing. While the electromechanical and electrochemical response of carbon nanotube
composites can be experimentally characterized, there still lacks a fundamental understanding of how the conductivity of
carbon nanotube composites is spatially distributed and how it depends on external stimuli. In this study, electrical
impedance tomography is proposed for spatial characterization of the conductivity of carbon nanotube composite thin
films. The method proves promising for both assessment of as-fabricated thin film quality as well as for two-dimensional
sensing of thin film response to mechanical strain and exposure to pH environments.

This study examines the potential use of wireless communication and embedded computing technologies within realtime
structural control applications. Based on the implementation of the prototype WiSSCon system in a three story steel
test structure with significant eccentricity, the centralized control architecture is implemented to mitigate the lateral and
torsional response of the test structure using two MR dampers installed in the first story. During the test, a large
earthquake time history is applied (El Centro earthquake) at the structure base using a shaking table. Three major
performance attributes of the wireless control system were examined: (1) validation of the reliability of wireless
communications for real-time structural control applications, (2) validation of a modified exponential damper model
embedded in the wireless sensors to operate the MR dampers, and (3) exploration of control effectiveness when using
WiSSCon in a centralized architectural configuration.

Structural health monitoring and control have attracted much research interest in the last few decades. Traditional
monitoring and control systems depend on the use of cables to transmit sensor data and actuation signals. With recent
advances in wireless communication technology, wireless networks can potentially offer a low-cost alternative to
traditional cable-based sensing and control systems. Another advantage of a wireless system is the ease of relocating
sensors and controllers, thus providing a flexible and reconfigurable system architecture. However, compared to a cable-based
system, wireless communication generally suffers more stringent limitations in terms of communication range,
bandwidth, latency, and reliability. This paper describes the architectural design of a prototype wireless structural
monitoring and control system. Although design criteria for sensing and control applications are different, state machine
concepts prove to be effective in designing simple yet efficient communication protocols for wireless structural sensing
and control networks. In wireless structural sensing applications, the design priority is to provide reliable data
aggregation, while in wireless structural control applications, the design priority is to guarantee real-time characteristics
of the system. The design methodologies have been implemented in a prototype wireless structural monitoring and
control system, and validated through a series of laboratory and field experiments.

The recent development of wireless sensors for structural health monitoring has revealed their strong dependency on
portable, limited battery supplies. Unlike current wireless sensors, passive radio frequency identification (RFID)
systems based on inductive coupling can wirelessly receive power from a portable reader while transmitting collected
data back. In this paper, preliminary results of a novel inductively coupled strain and corrosion sensor based upon
material fabrication techniques from the nanotechnology field are presented. By varying polyelectrolyte species during a
layer-by-layer fabrication process, carbon nanotube-polyelectrolyte multilayer thin film sensors sensitive to different
mechanical (e.g. strain) and chemical (e.g. pH) stimuli can be produced. Validation studies conducted with different
carbon nanotube thin films designed as either strain or pH sensors reveal high sensitivity and linear performance. When
coupled with a copper inductive coil antenna, resulting RFID-based sensors exhibit wirelessly readable changes in
resonant frequency and bandwidth. Furthermore, a carbon nanotube-gold nanocomposite thin film is fabricated and
patterned into a highly conductive coil structure to realize a novel thin film inductive antenna. Preliminary results
indicate that nanotube-gold nanocomposites exhibit resonance conditions, holding great promise for future RFID applications.

An extensive program of full-scale ambient vibration testing has been conducted to measure the dynamic response of a 240 meter cable-stayed bridge - Gi-Lu Bridge in Nan-Tou County, Taiwan. A MEMS-based wireless sensor system and a traditional microcomputer-based system were used to collect and analyze ambient vibration data. A total of four bridge modal frequencies and associated mode shapes were identified for cables and the deck structure within the frequency range of 0~2Hz. The experimental data clearly indicated the occurrence of many closely spaced modal frequencies. Most of the deck modes were found to be associated with the cable modes, implying a considerable interaction between the deck and cables. The results of the ambient vibration survey were compared to modal frequencies and mode shapes computed using three-dimensional finite element modeling of the bridge. For most modes, the analytical and the experimental modal frequencies and mode shapes compare quite well. Based on the findings of this study, a linear elastic finite element model for deck structures and beam element with P-Delta effect for the cables appear to be capable of capturing much of the complex dynamic behavior of the bridge with good accuracy.

To measure component-level structural responses due to external loading, strain sensors can provide detailed information pertaining to localized structural behavior. Although current metal foil strain sensors are capable of measuring strain deformations, they suffer from disadvantages including long-term performance issues when deployed in the field environment. This paper presents a novel carbon-nanotube polymer composite thin film that can be tailored for specific strain sensing properties. Beginning at the nano-scale, molecular manipulation of single-walled carbon nanotubes (SWNT) is performed to control chemical fabrication parameters as a means of establishing a relationship with macroscale bulk sensor properties. This novel strain sensor is fabricated using the Layer-by-Layer (LbL) self-assembly process. A rigorous experimental methodology is laid out to subject a variety of thin films to tensile-compressive cyclic loading. In particular, SWNT concentration, polyelectrolyte concentration, and film thickness are varied during the fabrication process to produce a variety of strain sensors. This study correlates fabrication parameters with bulk strain sensor properties; sensor properties including sensitivity (gauge factor), linearity, and hysteresis, are explored.

Bridge management officials have expressed a keen interest in the use of low-cost and easy-to-install wireless sensors to record bridge responses during short-term load testing. To illustrate the suitability of wireless sensors for short-term monitoring of highway bridges, a wireless monitoring system is installed upon the Grove Street Bridge to monitor structural responses during static and dynamic load testing. Specifically, load testing of the Grove Street Bridge is conducted after its construction to validate the behavior of a novel jointless bridge deck constructed from a high-performance fiber reinforced cementitious composite (HPFRCC) material. A heterogeneous array of sensing transducers are installed in the bridge including metal foil strain gages, accelerometers and linear variable differential transducers (LVDTs). First, the acceleration response of the bridge is monitored by the wireless system during routine traffic loading. Modal parameters (modal frequencies and mode shapes) are calculated by the wireless sensors so that an analytical model of the bridge constructed in a standard commercial finite element package can be updated off-line. Next, the bridge is closed to traffic and trucks of known weight are parked on the bridge to induce static deformations. The installation strategy of the wireless monitoring system during static load testing is optimized to monitor the strain and rotation response of the HPFRCC deck. The measured static response of the deck is compared to that predicted by the updated analytical model.

In recent years, substantial research has been conducted to advance structural control as a direct means of mitigating the
dynamic response of civil structures. In parallel to these efforts, the structural engineering field is currently exploring
low-cost wireless sensors for use in structural monitoring systems. To reduce the labor and costs associated with
installing extensive lengths of coaxial wires in today's structural control systems, wireless sensors are being considered
as building blocks of future systems. In the proposed system, wireless sensors are designed to perform three major tasks
in the control system; wireless sensors are responsible for the collection of structural response data, calculation of
control forces, and issuing commands to actuators. In this study, a wireless sensor is designed to fulfill these tasks
explicitly. However, the demands of the control system, namely the need to respond in real-time, push the limits of
current wireless sensor technology. The wireless channel can introduce delay in the communication of data between
wireless sensors; in some rare instances, outright data loss can be experienced. Such issues are considered an intricate
part of this feasibility study. A prototype Wireless Structural Sensing and Control (WiSSCon) system is presented
herein. To validate the performance of this prototype system, shaking table experiments are carried out on a half-scale
three story steel structure in which a magnetorheological (MR) damper is installed for real-time control. In comparison
to a cable-based control system installed in the same structure, the performance of the WiSSCon system is shown to be
effective and reliable.

In recent years, a new class of cementitious composite has been proposed for the design and construction of durable civil structures. Termed engineered cementitious composites (ECC), ECC utilizes a low volume fraction of short fibers (polymer, steel, carbon) within a cementitious matrix resulting in a composite that strain hardens when loaded in tension. By refining the mechanical properties of the fiber-cement interface, the material exhibits high tolerance to damage. This study explores the electrical properties of ECC materials to monitor their performance and health when employed in the construction of civil structures. In particular, the conductivity of ECC changes in proportion to strain indicating that the material is piezoresistive. In this paper, the piezoresistive properties of various ECC composites are thoroughly explored. To measure the electrical resistance of ECC structures in the field, a low-cost wireless active sensing unit is proposed. The wireless active sensing unit is capable of applying DC and AC voltage signals to ECC elements while simultaneously measuring their corresponding voltages away from the signal input. By locally processing the corresponding input-output electrical signals recorded by the wireless active sensing units, the magnitude of strain in ECC elements can be calculated. In addition to measuring strain, the study seeks to correlate changes in ECC electrical properties to the magnitude of crack damage witnessed in tested specimens. A large number of ECC specimens are tested in the laboratory including a large-scale ECC bridge pier laterally loaded under cyclically repeated drift reversals. The novel self-sensing properties of ECC exploited by a wireless monitoring system hold tremendous promise for the advancement of structural health monitoring of ECC structures.

In this study, a new wireless sensing unit for operation within an automated Structural Health Monitoring (SHM) system is proposed, designed and validated. The design of the wireless sensing unit emphasizes minimization of its power consumption characteristics to ensure it is suited for long-term field deployment in civil structures. The wireless modem integrated with the unit has a long communication range that permits wireless sensors to be spaced over 100m apart. A multi-channel high-resolution analog-to-digital converter is included within each sensing unit to provide flexibility for high-fidelity data collection. A key feature of the wireless sensing unit design is the inclusion of a sophisticated computing core that is capable of locally executing engineering algorithms in real-time. As part of the embedded software, a novel communication protocol is written that can accomplish low-latency communications for accurate time synchronization between spatially distributed wireless sensors. To illustrate the capabilities of the wireless monitoring platform, including the execution of extensive computational tasks, a prototype system is fabricated and tested in the laboratory and field. As part of validating the system performance in the field, the vertical acceleration response of the Geumdang Bridge under traffic loading is measured by 14 wireless sensing unit prototypes.

Concrete bridge piers are a common structural element employed in the design of bridges and elevated roadways. In order to ensure adequate behavior under earthquake-induced displacements, extensive reinforcement detailing in the form of closely spaced ties or spirals is necessary, leading to congestion problems and difficulties during concrete casting. Further, costly repairs are often necessary in bridge piers after a major earthquake which in some cases involve the total or partial shutdown of the bridge. In order to increase the damage tolerance while relaxing the transverse reinforcement requirements of bridge piers, the use of high-performance fiber reinforced cementitious composites (HPFRCC) in earthquake-resistant bridge piers is explored. HPFRCCs are a relatively new class of cementitious material for civil structures with tensile strain-hardening behavior and high damage tolerance. To monitor the behavior of this new class of material in the field, low-cost wireless monitoring technologies will be adopted to provide HPFRCC structural elements the capability to accurately monitor their performance and health. In particular, the computational core of a wireless sensing unit can be harnessed to screen HPFRCC components for damage in real-time. A seismic damage index initially proposed for flexure dominated reinforced concrete elements is modified to serve as an algorithmic tool for the rapid assessment of damage (due to flexure and shear) in HPFRCC bridge piers subjected to large shear reversals. Traditional and non-traditional sensor strategies of an HPFRCC bridge pier are proposed to optimize the correlation between the proposed damage index model and the damage observed in a circular pier test specimen. Damage index models are shown to be a sufficiently accurate rough measure of the degree of local-area damage that can then be wirelessly communicated to bridge officials.

This paper presents the preliminary findings of a study on data and system identification results (derived from collected data) in a wireless sensing environment. The goal of this study is to understand how various hardware design choices and operational conditions affect the quality of the data and accuracy of the identified results; the focus of this paper is packet and data loss. A series of experimental investigations are carried out using a laboratory shaking table instrumented with off-the-shelf Micro-Electro-Mechanical Systems (MEMS) accelerometers. A wireless sensing unit is developed to interface with these wired analog accelerometers to enable wireless data transmission. To reduce the overall design variance and aid convenient application in civil infrastructure health monitoring, this wireless unit is built with off-the-shelf microcontroller and radio development boards. The anti-aliasing filter and analog-to-digital convectors (ADC) are the only customized components in the hardware. By varying critical hardware configurations, including using analog accelerometers of different commercial brands, taking various designs for the anti-aliasing filter, and adopting ADCs with different resolutions, shaking table tests are repeated, the collected data are processed, and the results are compared. Operational conditions such as sampling rate and wireless data transmitting range are also altered separately in the repeated testing. In all of the cases tested, data is also collected using a wire-based data acquisition system to serve as a performance baseline for evaluation of the wireless data transmission performance. Based on this study, the challenges in the hardware design of wireless sensing units and data processing are identified.

The identification of damage in structural systems, including characterization of damage location and severity, is of extreme interest to the structural engineering profession. To date, many damage detection methods have been proposed that utilize global structural response measurements in the time and frequency domains to hypothesize the existence of structural damage. The accuracy and robustness of current damage detection methodologies could be improved through the use of active sensors. Active sensors, such as piezoelectric pads, impart low-energy acoustic excitations into structural elements and can record the corresponding system behavior. In this study, a novel methodology utilizing the input-output behavior of actively sensed structural elements is proposed. The poles of ARX time-series models describing modal frequencies and damping ratios are plotted upon the discrete-time complex plane and Perceptron linear classifiers employed to determine if poles of the structural element in an unknown state (damaged or undamaged) can be separated with those of the undamaged structure. If poles of the unknown state are separable from those of the undamaged state, the system is diagnosed as damaged. A simple cantilevered aluminum plate damaged by hack saw cuts is actively sensed by piezoelectric pads to show the efficacy of the proposed damage detection methodology. Furthermore, the number of misclassified poles and the final value of the Perceptron criterion function can be shown to be correlated to the severity of the damage.

Many academic and commercial researchers are exploring the design and deployment of wireless sensors that can be used for structural monitoring. The concept of intelligent wireless sensors can be further extended to include actuation capabilities. In this study, the design of a wireless sensing unit that has the capability to command active sensors and actuators is proposed for structural monitoring applications. Active sensors are sensors that can input excitations into a structural system and simultaneously monitor the corresponding system's response. The computational core of the wireless active sensing unit is capable of interrogating response data in real time and can be used to execute embedded damage detection analyses. With high-order vibration modes of structural elements exhibiting greater sensitivity to damage than global structural modes, wireless active sensors can play a major role in a structural health monitoring system because they are capable of exciting high-order modes. A computational framework for analyzing piezoelectric based active sensor signals for indications of structural damage is proposed. For illustration, a simple aluminum plate with piezoelectric active sensors mounted to its surface is used.

This paper addresses the development of a robust, low-cost, low power, and high performance autonomous
wireless monitoring system for civil assets such as large facilities, new construction, bridges, dams, commercial
buildings, etc. The role of the system is to identify the onset, development, location and severity of structural
vulnerability and damage. The proposed system represents an enabling infrastructure for addressing structural
vulnerabilities specifically associated with homeland security.
The system concept is based on dense networks of “intelligent” wireless sensing units. The fundamental
properties of a wireless sensing unit include: (a) interfaces to multiple sensors for measuring structural and
environmental data (such as acceleration, displacements, pressure, strain, material degradation, temperature, gas agents,
biological agents, humidity, corrosion, etc.); (b) processing of sensor data with embedded algorithms for assessing
damage and environmental conditions; (c) peer-to-peer wireless communications for information exchange among units(thus enabling joint “intelligent” processing coordination) and storage of data and processed information in servers for
information fusion; (d) ultra low power operation; (e) cost-effectiveness and compact size through the use of low-cost
small-size off-the-shelf components. An integral component of the overall system concept is a decision support
environment for interpretation and dissemination of information to various decision makers.

Wireless health monitoring schemes are innovative techniques, which effectively remove the disadvantages associated with current wire-based sensing systems, i.e., high installation and upkeep costs. However, recorded data sets may have relative time-delays due to the blockage of sensors or inherent internal clock errors. In this paper, two algorithms are proposed for the synchronization of the recorded asynchronous data measured from sensing units of a wireless monitoring system. In the first algorithm, the input signal to a structure is measured. Time-delay between an output measurement and the input is identified based on the minimization of errors of the ARX (auto-regressive model with exogenous input) models for the input-output pair recordings. The second algorithm is applicable when a structure is subject to ambient excitation and only output measurements are available. ARMAV (auto-regressive moving average vector) models are constructed from two output signals and the time-delay between them is evaluated based on the minimization of errors of the ARMAV models. The proposed algorithms are verified by simulation data and recorded seismic response data from multi-story buildings.

In this paper, we make a brief study of some of the important requirements of a structural monitoring system for civil infrastructures and explain the key issues that are faced in the design of a suitable wireless monitoring strategy. Two-tiered wireless sensor network architecture is proposed as a solution to these issues and the protocol used for the communication in this network is described. The power saving strategies at various levels, from the network architecture, to communication protocol, to the sensor unit architecture are explained. A detailed analysis of the network is done and the implementation of this network in a laboratory setting is described.

A state-of-art design of a wireless sensing unit, which serves as the fundamental building block of wireless modular monitoring systems (WiMMS), has been optimized for structural monitoring sensing applications. Employing wireless communications as a primary means of data transfer, the high-cost but fragile cables of traditional tethered monitoring systems is eradicated resulting in a low-cost yet flexible monitoring infrastructure. An additional innovation is the inclusion of advanced embedded microcontrollers to accommodate the computational tasks of engineering and decision support analysis. To quantify the performance of the wireless sensing unit, field validation upon a full-scale benchmark structure is undertaken. The Alamosa Canyon Bridge in New Mexico is instrumented with wireless sensing units and a traditional cable-based monitoring system in parallel. Forced vibrations are applied to the bridge and monitored using both (wireless and tethered) data acquisition systems. Recorded time-history measurements are used to identify the modal properties of the structural system. The performance of the wireless sensing units is compared to that of the commercial wire-based monitoring system.

A wireless sensing unit for use in a Wireless Modular Monitoring System (WiMMS) has been designed and constructed. Drawing upon advanced technological developments in the areas of wireless communications, low-power microprocessors and micro-electro mechanical system (MEMS) sensing transducers, the wireless sensing unit represents a high-performance yet low-cost solution to monitoring the short-term and long-term performance of structures. A sophisticated reduced instruction set computer (RISC) microcontroller is placed at the core of the unit to accommodate on-board computations, measurement filtering and data interrogation algorithms. The functionality of the wireless sensing unit is validated through various experiments involving multiple sensing transducers interfaced to the sensing unit. In particular, MEMS-based accelerometers are used as the primary sensing transducer in this study's validation experiments. A five degree of freedom scaled test structure mounted upon a shaking table is employed for system validation.

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Advanced PhotonicsJournal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews